Khairulmizam Samsudin

Dr. Khairulmizam Samsudin


Research Focus & Metrics

Academic Career Length

12Years (2015-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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28 September 2015 in IEEE Transactions on Circuits and Systems II: Express Briefs
Verified
Journal Article
Closed access
WFWameedh Flayyih
Wameedh Flayyih
KSKhairulmizam Samsudin
Khairulmizam Samsudin
SHShaiful Hashim
Shaiful Hashim
YIYehea I. Ismail
Yehea I. Ismail
FRFakhrul Zaman Rokhani
Fakhrul Zaman Rokhani
The presence of different noise sources and continuous increase in crosstalk in the deep submicrometer technology raised concerns for on-chip communication reliability, leading to the incorporation of crosstalk avoidance techniques in error control coding schemes. This brief proposes joint crosstalk avoidance with adaptive error control scheme to reduce the power consumption by providing appropriate communication resiliency based on runtime noise level. By switching between shielding and duplication as the crosstalk avoidance technique and between hybrid automatic repeat request and forward error correction as the error control policies, three modes of error resiliencies are provided. The results show that, in reduced mode, the scheme achieves up to 25.3% power savings at 3-mm wire length as compared to the original nonadaptive scheme at the cost of only 3.4% power overhead in high protection mode.

28 December 2016 in Indian Journal of Science and Technology
Verified
Journal Article
Open access
PDF file
NANasraddin Ahmed Salem Al-Maweri
Nasraddin Ahmed Salem Al-Maweri
WAWan Azizun Wan Adnan
Wan Azizun Wan Adnan
ARAbdul Rahman Ramli
Abdul Rahman Ramli
KSKhairulmizam Samsudin
Khairulmizam Samsudin
SRSharifah Mumtazah Syed Ahmad Abdul Rahman
Sharifah Mumtazah Syed Ahmad Abdul Rahman
Robust Digital Text Watermarking Algorithm based on Unicode Extended Characters

22 January 2022 in Wireless Personal Communications
Verified
Journal Article
Closed access
PDF file
MAMohammed Al-Mekhlafi
Mohammed Al-Mekhlafi
KSKhairulmizam Samsudin
Khairulmizam Samsudin
MRMohd Fadlee A. Rasid
Mohd Fadlee A. Rasid
FRFakhrul Zaman Rokhani
Fakhrul Zaman Rokhani

1 January 2025 in Journal of Communications Software and Systems
Verified
Journal Article
Open access
PDF file
ONOmer Abdulhaleem Naser
Omer Abdulhaleem Naser
SMSharifah Mumtazah
Sharifah Mumtazah
KSKhairulmizam Samsudin
Khairulmizam Samsudin
MHMarsyita Hanafi
Marsyita Hanafi
SBSiti Mariam Binti
Siti Mariam Binti
NZNor Zarina Zamri
Nor Zarina Zamri
As computer vision and machine learning advance, face detection has become a major focus. Face recognition has several methods and models. Every implementation starts with face detection. Haar Cascades and Multi-task Cascaded Convolutional Networks (MTCNN) are compared for facial pose variation robustness. This research will examine how well these two models detect faces in yaw postures from -90 to +90 degrees. Many studies have contrasted these two models, but the yaw poses of faces were not addressed due to the scarcity of datasets with systematic degrees of face orientation. Thus, the UPM face dataset, created at the UPM embedded systems lab using developed equipment to produce high-resolution photographs and a systematic range of face orientations from -90 to 90 degrees, was used to evaluate the range of degrees these two models can reach. UPM includes 100 students with different yaw angles and occlusions (masks, glasses, or both). The results reveal that MTCNN is the best for detecting faces with yaw poses only, masks, glasses, and both at all degrees (-90 to +90) with 100%, 99.9%, 96.4%, and 80% accuracy. Instead, Haar cascades were 92.5%, 67.3%, 80.4%, and 76.3% accurate.